{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ebd66700",
   "metadata": {},
   "source": [
    "## Demo_UMAP\n",
    "This is a demo for visualizing the UMAP of a Neuron Network\n",
    "\n",
    "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b950f4fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "! python ../../attack/badnet.py --save_folder_name badnet_demo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f81f973",
   "metadata": {},
   "source": [
    "or run the following command in your terminal\n",
    "\n",
    "```python attack/badnet.py --save_folder_name badnet_demo```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac8e57d",
   "metadata": {},
   "source": [
    "#### Difference between UMAP and T-SNE\n",
    "* Both of them can be used for dimension reduction, i.e., reducing the dimension of given features. But, UMAP is much faster than T-SNE which allows us to use more samples for a more comprehensive view."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87bd9f5a",
   "metadata": {},
   "source": [
    "### Step 1: Import modules and set arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "71b7087b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "import yaml\n",
    "import torch\n",
    "import numpy as np\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "sys.path.append(\"../\")\n",
    "sys.path.append(\"../../\")\n",
    "sys.path.append(os.getcwd())\n",
    "from visual_utils import *\n",
    "from utils.aggregate_block.dataset_and_transform_generate import (\n",
    "    get_transform,\n",
    "    get_dataset_denormalization,\n",
    ")\n",
    "from utils.aggregate_block.fix_random import fix_random\n",
    "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n",
    "from utils.save_load_attack import load_attack_result\n",
    "from utils.defense_utils.dbd.model.utils import (\n",
    "    get_network_dbd,\n",
    "    load_state,\n",
    "    get_criterion,\n",
    "    get_optimizer,\n",
    "    get_scheduler,\n",
    ")\n",
    "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2fb719c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Basic setting: args\n",
    "args = get_args(True)\n",
    "\n",
    "########## For Demo Only ##########\n",
    "args.yaml_path = \"../../\"+args.yaml_path\n",
    "args.result_file_attack = \"badnet_demo\"\n",
    "######## End For Demo Only ##########\n",
    "\n",
    "with open(args.yaml_path, \"r\") as stream:\n",
    "    config = yaml.safe_load(stream)\n",
    "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n",
    "args.__dict__ = config\n",
    "args = preprocess_args(args)\n",
    "fix_random(int(args.random_seed))\n",
    "\n",
    "save_path_attack = \"../..//record/\" + args.result_file_attack\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f959b510",
   "metadata": {},
   "source": [
    "### Step 2: Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8b67ac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "loading...\n",
      "max_num_samples is given, use sample number limit now.\n",
      "subset bd dataset with length:  50000\n",
      "Create visualization dataset with \n",
      " \t Dataset: bd_train \n",
      " \t Number of samples: 50000  \n",
      " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "# Load result\n",
    "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n",
    "selected_classes = np.arange(args.num_classes)\n",
    "\n",
    "# Select classes to visualize\n",
    "if args.num_classes>args.c_sub:\n",
    "    selected_classes = np.delete(selected_classes, args.target_class)\n",
    "    selected_classes = np.random.choice(selected_classes, args.c_sub-1, replace=False)\n",
    "    selected_classes = np.append(selected_classes, args.target_class)\n",
    "\n",
    "# keep the same transforms for train and test dataset for better visualization\n",
    "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n",
    "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n",
    "\n",
    "args.visual_dataset = \"bd_train\"\n",
    "args.n_sub = 50000\n",
    "# Create dataset\n",
    "if args.visual_dataset == 'mixed':\n",
    "    bd_test_with_trans = result_attack[\"bd_test\"]\n",
    "    visual_dataset = generate_mix_dataset(bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub)\n",
    "elif args.visual_dataset == 'clean_train':\n",
    "    clean_train_with_trans = result_attack[\"clean_train\"]\n",
    "    visual_dataset = generate_clean_dataset(clean_train_with_trans, selected_classes, max_num_samples=args.n_sub)\n",
    "elif args.visual_dataset == 'clean_test':\n",
    "    clean_test_with_trans = result_attack[\"clean_test\"]\n",
    "    visual_dataset = generate_clean_dataset(clean_test_with_trans, selected_classes, max_num_samples=args.n_sub)\n",
    "elif args.visual_dataset == 'bd_train':  \n",
    "    bd_train_with_trans = result_attack[\"bd_train\"]\n",
    "    visual_dataset = generate_bd_dataset(bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n",
    "else:\n",
    "    assert False, \"Illegal vis_class\"\n",
    "\n",
    "print(f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)}  \\n \\t Selected classes: {selected_classes}')\n",
    "\n",
    "# Create data loader\n",
    "data_loader = torch.utils.data.DataLoader(\n",
    "    visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False\n",
    ")\n",
    "\n",
    "# Create denormalization function\n",
    "for trans_t in data_loader.dataset.wrap_img_transform.transforms:\n",
    "    if isinstance(trans_t, transforms.Normalize):\n",
    "        denormalizer = get_dataset_denormalization(trans_t)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f652e5",
   "metadata": {},
   "source": [
    "### Step 3: Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ff67e7b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load model preactresnet18 from badnet_demo\n"
     ]
    }
   ],
   "source": [
    "# Load model\n",
    "model_visual = generate_cls_model(args.model, args.num_classes)\n",
    "model_visual.load_state_dict(result_attack[\"model\"])\n",
    "model_visual.to(args.device)\n",
    "# !!! Important to set eval mode !!!\n",
    "model_visual.eval()\n",
    "print(f\"Load model {args.model} from {args.result_file_attack}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc952077",
   "metadata": {},
   "source": [
    "### Step 4: Plot UMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "94612903",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting UMAP\n",
      "Choose layer layer4.1.conv2 from model preactresnet18\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "############## UMAP ##################\n",
    "print(\"Plotting UMAP\")\n",
    "\n",
    "# Choose layer for feature extraction\n",
    "module_dict = dict(model_visual.named_modules())\n",
    "target_layer = module_dict[args.target_layer_name]\n",
    "print(f'Choose layer {args.target_layer_name} from model {args.model}')\n",
    "\n",
    "# Get features\n",
    "features, labels, poi_indicator = get_features(args, model_visual, target_layer, data_loader)\n",
    "\n",
    "# General plotting parameters\n",
    "custom_palette = sns.color_palette(\"hls\", np.unique(labels).shape[0])\n",
    "classes = args.class_names\n",
    "\n",
    "# Setting parameters for Poisoned Samples\n",
    "# use poi_indicator==1 to avoid some datatype issue for indexing\n",
    "if np.sum(poi_indicator)>0:\n",
    "    # Label: args.num_classes\n",
    "    labels[poi_indicator==1]=args.num_classes\n",
    "    # Class Name: poisoned\n",
    "    classes += [\"poisoned\"]\n",
    "    # Color: Black\n",
    "    custom_palette += [(0.0, 0.0, 0.0)] \n",
    "\n",
    "sort_idx = np.argsort(labels)\n",
    "features = features[sort_idx]\n",
    "labels = labels[sort_idx]\n",
    "label_class = [classes[i].capitalize() for i in labels]\n",
    "\n",
    "# Plot T-SNE\n",
    "fig = umap_fig(\n",
    "    features,\n",
    "    label_class,\n",
    "    title=\"UMAP Embedding\",\n",
    "    xlabel=\"Dim 1\",\n",
    "    ylabel=\"Dim 2\",\n",
    "    custom_palette=custom_palette,\n",
    "    size=(10, 10),\n",
    "    mark_size = 0.6,\n",
    "    alpha = 1\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('py38')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13 (default, Oct 21 2022, 23:50:54) \n[GCC 11.2.0]"
  },
  "vscode": {
   "interpreter": {
    "hash": "6869619afde5ccaa692f7f4d174735a0f86b1f7ceee086952855511b0b6edec0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
